2017
DOI: 10.19044/esj.2017.v13n33p340
|View full text |Cite
|
Sign up to set email alerts
|

Investigating the Performance of Smote for Class Imbalanced Learning: A Case Study of Credit Scoring Datasets

Abstract: Classification of datasets is one of the major issues encountered by the data mining community. This problem heightens when the real world datasets is also imbalanced in nature. A dataset happens to be imbalanced when the numbers of observations belonging to rare class are greatly outnumbered by the observations of another class. Class with greater number of observation is called the majority or the negative class, while the other with rare observations is referred to as the minority or the positive class. Lit… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
4
0
1

Year Published

2019
2019
2024
2024

Publication Types

Select...
6
2
1

Relationship

0
9

Authors

Journals

citations
Cited by 9 publications
(5 citation statements)
references
References 29 publications
0
4
0
1
Order By: Relevance
“…Ref. [83] elucidates that data sets with extreme imbalances exhibit suboptimal performance even after the generation of synthetic samples. Additionally, ref.…”
Section: Classification Resultsmentioning
confidence: 99%
“…Ref. [83] elucidates that data sets with extreme imbalances exhibit suboptimal performance even after the generation of synthetic samples. Additionally, ref.…”
Section: Classification Resultsmentioning
confidence: 99%
“…Clearly, class label 0 is the minority class. The danger of class imbalance is that conventionally, algorithms tend to become biased by predicting the overall accuracy towards the class with bigger observations [39]. To address this, we utilized a supervised instance filter called synthetic minority oversampling technique (SMOTE).…”
Section: Data Imbalancementioning
confidence: 99%
“…Data tidak seimbang atau lebih sering disebut imbalanced data, adalah kondisi pada saat data memiliki rasio yang tidak seimbang antara satu kelas dengan kelas yang lain, sehingga terdapat kelas mayoritas (dengan data yang banyak) dan kelas minoritas (dengan data sedikit) [1]. Sulit untuk membuat prediksi pada dataset yang tidak seimbang karena pengklasifikasi cenderung mendeteksi kelas mayoritas daripada kelas minoritas.…”
Section: Pendahuluanunclassified